The number of pixels of an image sensor disposed in an imaging apparatus has been remarkably increased in recent years. The resolution of the imaging device has been improved to the level of a smooth granularity which human's eyes cannot recognize. Under such circumstances, requirements for picture quality have been changed from higher resolution to reduction of a sense of noise. To reduce a sense of noise, an imaging apparatus performs noise reduction (a noise reduction process) for an image signal captured by an image sensor.
A noise reduction apparatus which performs the noise reduction using epsilon (ε) filtering is described in a patent document disclosed as Japanese Patent Application Unexamined Publication No. HEI 6-86104.
Next, with reference to FIG. 1A and FIG. 1B, the noise reduction using the ε filtering will be described. FIG. 1A shows pixel levels of a plurality of adjacent pixels (these levels may be also referred to as pixel values) in a flat portion of an image. In this example, it is assumed that an attention pixel 101 as an object used for noise reduction, three adjacent pixels 102, 103, and 104 on the left thereof, and three adjacent pixels 105, 106, and 107 on the right thereof are designated as a detection region so as to smoothen these pixels. In this example, it is assumed that the attention pixel 101 and the adjacent pixel 102 to adjacent pixel 107 are pixels of the same color component.
The pixel level of each of the adjacent pixel 102 to adjacent pixel 107 in the detection region in the non-noise state is different from the pixel level of the corresponding pixel in the noise state (this difference is referred to as level difference). This level difference is indicated as noise. When the absolute value of the level difference of the attention pixel 101 and each of the adjacent pixels in the detection region is within a threshold value which is designated as the pixel value of the attention pixel 101, it can be determined that the adjacent pixel 102 to adjacent pixel 107 be able to be used for the noise reduction. A filter process is performed by calculating a mean value of the pixel values of the attention pixel 101 and the adjacent pixel 102 to adjacent pixel 107 (this calculation may be referred to as calculation of arithmetic mean). In the example shown in FIG. 1A, the detection region is in the left and right directions of the attention pixel 101. However, as long as the detection region is plane, it may be in the upper and lower directions, left diagonal directions, and/or right diagonal directions of the attention pixel 101.
In the noise reduction using the ε filtering, it is determined that when the absolute value of the difference of the pixel levels of an attention pixel and an adjacent pixel in a detection region is within a predetermined threshold value, they be correlated with respect to a signal component. The arithmetic mean of the pixel levels of the attention pixel and the adjacent pixels determined to be correlated with the attention pixel is calculated. On the other hand, in the noise reduction using the ε filtering, a pixel whose pixel level is largely different from that of an attention pixel for example an edge portion (contour portion) is not used. Thus, in a flat portion of an image shown in FIG. 1A, the noise reduction can be performed in such a manner that an edge does not become dull and deterioration of frequency characteristics is suppressed as much as possible.
However, the foregoing noise reduction of the related art has the following problems. FIG. 1B shows pixel levels of pixels in a ramp portion of an image of which the pixel levels of the pixels gradually vary on a plane. As shown in FIG. 1B, among a plurality of adjacent pixels in a detection region, only an adjacent pixel 204 and an adjacent pixel 205 are pixels whose absolute value of the level difference against an attention pixel does not exceed a threshold value. Thus, when a ramp portion of an image is smoothened with the attention pixel 201 and the adjacent pixels 204 and 205, the number of pixels to be used to calculate the arithmetic mean is smaller than that in the flat portion of the image. As a result, the effect of the noise reduction in the ramp portion is not sufficient. In addition, when the arithmetic mean process is performed with the pixel levels of the attention pixel and its adjacent pixels, the frequency characteristics of an output image deteriorate. This does not result in a serious problem in a flat portion of an image. However, in a high frequency region of an image, when noise is removed, frequency characteristics deteriorate and thereby the output image becomes dull.
When an image contains both a flat portion and a ramp portion, although the effect of the noise reduction for the flat portion is obtained, that for the ramp portion is not sufficient. As a result, a sense of noise becomes strong in the ramp portion. In addition, in the noise reduction of the related art, depending on adjacent pixels used for noise reduction, the center of gravity of an attention pixel is moved after the noise reduction is performed. As a result, the linearity of the image remarkably deteriorates. In addition, if an attention pixel contains sharp noise for example impulse-shaped noise, the effect of the noise reduction cannot be expected.
In addition, the noise reduction is performed for an attention pixel and its adjacent pixels of the same color component. However, depending on a color filter used for the image sensor, the number of adjacent pixels having the same color component as the attention pixel may be decreased. For example, when a color filter having three primary color signals (R (Red), G (Green), and B (Blue)) is used, with consideration of visibility characteristics of human's eyes, the color filter has more G filters than R filters and B filters. Thus, the numbers of R filters and B filters are relatively smaller than the number of G filters. As a result, if an attention pixel used for the noise reduction is for example the R component, it is necessary to widen the detection region so that the number of pixels of the R component is increased. However, in this case, since the detection region is widened, it is necessary to increase the storage capacity of a line memory. Thus, the circuit scale adversely increases. In addition, when the detection region is widened, the correlation between an attention pixel and its adjacent pixels may become weak, resulting in deteriorating the effect of the noise reduction.